蝴蝶优化算法，MATLAB代码，无运行文件，仅供交流学习

%Butterfly optimization algorithm % AroraS,SinghS.Butterflyoptimizationalgorithm:Anovel approach for global optimization [J]. % SoftComputing,2019,23 (3):715-734. function [fmin,best_pos,Convergence_curve]=BOA(n,N_iter,Lb,Ub,dim,fobj) % n is the population size % N_iter represnets total number of iterations p=0.8; % probabibility switch power_exponent=0.1; sensory_modality=0.01; %Initialize the positions of search agents Sol=initialization(n,dim,Ub,Lb); for i=1:n Fitness(i)=fobj(Sol(i,:)); end % Find the current best_pos [fmin,I]=min(Fitness); best_pos=Sol(I,:); S=Sol; % Start the iterations -- Butterfly Optimization Algorithm for t=1:N_iter for i=1:n% Loop over all butterflies/solutions %Calculate fragrance of each butterfly which is correlated with objective function Fnew=fobj(S(i,:)); FP=(sensory_modality*(Fnew^power_exponent)); %Global or local search if rand<p dis = rand * rand * best_pos - Sol(i,:); %Eq. (2) in paper S(i,:)=Sol(i,:)+dis*FP; else % Find random butterflies in the neighbourhood epsilon=rand; JK=randperm(n); dis=epsilon*epsilon*Sol(JK(1),:)-Sol(JK(2),:); S(i,:)=Sol(i,:)+dis*FP; %Eq. (3) in paper end % Check if the simple limits/bounds are OK S(i,:)=simplebounds(S(i,:),Lb,Ub); % Evaluate new solutions Fnew=fobj(S(i,:)); %Fnew represents new fitness values % If fitness improves (better solutions found), update then if (Fnew<=Fitness(i)) Sol(i,:)=S(i,:); Fitness(i)=Fnew; end % Update the current global best_pos if Fnew<=fmin best_pos=S(i,:); fmin=Fnew; end end Convergence_curve(t,1)=fmin; %Update sensory_modality sensory_modality=sensory_modality_NEW(sensory_modality, N_iter); end end % Boundary constraints function s=simplebounds(s,Lb,Ub) % Apply the lower bound ns_tmp=s; I=ns_tmp<Lb; ns_tmp(I)=Lb; % Apply the upper bounds J=ns_tmp>Ub; ns_tmp(J)=Ub; % Update this new move s=ns_tmp; end function y=sensory_modality_NEW(x,Ngen) y=x+(0.025/(x*Ngen)); end